Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
Open accessAbstract
Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
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Optimal transport is a technique for measuring distances between equally sized samples that has been applied to scRNA-seq.
“Optimal transport is a technique for measuring distances between equally sized samples that has been applied to a wide variety of data analysis tasks, including scRNA-seq (Schiebinger et al. 2019).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Schiebinger et al. 2019
The Results section (“Reconstruction of probabilistic trajectories by Optimal Transport”) confirms that Waddington-OT applies optimal transport to scRNA-seq data. It does not describe the technique as measuring distances only between equally sized samples; instead, it says the method modifies classical mass-conservation constraints to accommodate cell growth and death. Page numbers are not provided in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSeveral distance measures for scRNA-seq have been explored, including Wasserstein distance, maximum mean discrepancy, neighborhood-based measures, and energy distance.
“Multiple distance measures for scRNA-seq have been explored by the single-cell community in recent years, including Wasserstein distance computed in an optimal transport framework22,23, maximum mean discrepancy24, neighborhood-based measures17,18, and energy distance (E-distance)20.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [23]
Shisa8 was expressed near the base of acetylated tubulin tufts in multi-ciliated epithelial cells.
“Shisa8, a reprogramming regulator (Schiebinger et al., 2019), was also expressed near the base of acetylated tubulin tufts in multi-ciliated epithelial cells (Figure 2N,O).”
From A cellular and spatial map of the choroid plexus across brain ventricles and ages (2021) · cites this paper as Schiebinger et al., 2019
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Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673 · see citing works
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Optimal Transport: Old and New
Villani, C. (2013) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-3-540-71050-9 · Cited by 3,838 · see citing works
The ground state of embryonic stem cell self-renewal
Ying, Q.-L., Wray, J., Nichols, J., Batlle-Morera, L., Doble, B., Woodgett, J., Cohen, P., & Smith, A. (2008) · Nature · doi:10.1038/nature06968 · Cited by 3,442 · see citing works
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098679 · Cited by 2,909 · see citing works
Optimal Transport for Applied Mathematicians
Santambrogio, F. (2015) · Progress in nonlinear differential equations and their applications · doi:10.1007/978-3-319-20828-2 · Cited by 2,322 · see citing works
Gradient Flows: In Metric Spaces and in the Space of Probability Measures
Ambrosio, L., Gigli, N., & Savaré, G. (2005) · doi:10.1007/978-3-7643-8722-8 · Cited by 2,312 · see citing works
THE VARIATIONAL FORMULATION OF THE FOKKER-PLANCK EQUATION
Jordan, R., Kinderlehrer, D., & Otto, F. (1996) · SIAM Journal on Mathematical Analysis · doi:10.1137/s0036141096303359 · Cited by 2,245 · see citing works
Waddington, C. H. (2014) · doi:10.4324/9781315765471 · Cited by 2,153 · see citing works
Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698 · see citing works
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Dissecting direct reprogramming through integrative genomic analysis
Mikkelsen, T. S., Hanna, J. H., Zhang, X., Ku, M., Wernig, M., Schorderet, P., Bernstein, B., Jaenisch, R., Lander, E. S., & Meissner, A. (2008) · Nature · doi:10.1038/nature07056 · Cited by 1,472 · see citing works
Self-citationThe Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology
(1958) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1958.tb86500.x · Cited by 1,244 · see citing works
Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma
Tirosh, I., Venteicher, A. S., Hebert, C. M., Escalante, L. E., Patel, A. P., Yizhak, K., Fisher, J. M., Rodman, C., Mount, C., Filbin, M. G., et al. (2016) · Nature · doi:10.1038/nature20123 · Cited by 1,216 · see citing works
Self-citationSequential expression of pluripotency markers during direct reprogramming of mouse somatic cells.
Brambrink, T., Foreman, R. K., Welstead, G., Lengner, C., Wernig, M., Suh, H., & Jaenisch, R. (2008) · Cell Stem Cell · doi:10.1016/j.stem.2008.01.004 · Cited by 892 · see citing works
Self-citationSingle-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876 · see citing works
Self-citationA Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells
Polo, J. M., Anderssen, E., Walsh, R. M., Schwarz, B. A., Nefzger, C. M., Lim, S. M., Borkent, M., Apostolou, E., Alaei, S., Cloutier, J., et al. (2012) · Cell · doi:10.1016/j.cell.2012.11.039 · Cited by 861 · see citing works
Self-citationSingle-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849 · see citing works
A decade of transcription factor-mediated reprogramming to pluripotency
Takahashi, K., & Yamanaka, S. (2016) · Nature reviews. Molecular cell biology · doi:10.1038/nrm.2016.8 · Cited by 815 · see citing works
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · see citing works
Self-citationOn the Translocation of Masses
Kantorovich, L. V. (2006) · Journal of Mathematical Sciences · doi:10.1007/s10958-006-0049-2 · Cited by 776 · see citing works
Tissue damage and senescence provide critical signals for cellular reprogramming in vivo
Mosteiro, L., Pantoja, C., Alcazar, N., Marión, R. M., Chondronasiou, D., Rovira, M., Fernandez-Marcos, P. J., Muñoz-Martin, M., Blanco-Aparicio, C., Pastor, J., et al. (2016) · Science · doi:10.1126/science.aaf4445 · Cited by 589 · see citing works
Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.09.011 · Cited by 570 · see citing works
Gata3 regulates trophoblast development downstream of Tead4 and in parallel to Cdx2
Ralston, A., Cox, B., Nishioka, N., Sasaki, H., Chea, E., Rugg‐Gunn, P. J., Guo, G., Robson, P., Draper, J. S., & Rossant, J. (2010) · Development · doi:10.1242/dev.038828 · Cited by 462 · see citing works
A survey of the Schr\"odinger problem and some of its connections with optimal transportPreprint
Léonard, C. (2013) · arXiv (Cornell University) · Cited by 379 · see citing works
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
Mehta, S., Cronkite, D., Basavappa, M., Saunders, T. L., Adiliaghdam, F., Amatullah, H., Morrison, S. A., Pagan, J., Anthony, R., Tonnerre, P., et al. (2017) · Science immunology · doi:10.1126/sciimmunol.aal2192 · Cited by 368 · see citing works
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Herman, J. S., Sagar, & Grün, D. (2018) · Nature Methods · doi:10.1038/nmeth.4662 · Cited by 363 · see citing works
Determinants of trophoblast lineage and cell subtype specification in the mouse placenta
Simmons, D. G., & Cross, J. C. (2005) · Developmental Biology · doi:10.1016/j.ydbio.2005.05.010 · Cited by 357 · see citing works
Fundamental limits on dynamic inference from single-cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714723115 · Cited by 341 · see citing works
Scaling algorithms for unbalanced optimal transport problems
Chizat, L., Gabriel Peyré, A., Schmitzer, B., & Vialard, F. (2017) · Mathematics of Computation · doi:10.1090/mcom/3303 · Cited by 340 · see citing works
SPRING: a kinetic interface for visualizing high dimensional single-cell expression dataPreprint
Weinreb, C., Wolock, S. L., & Klein, A. M. (2017) · bioRxiv · doi:10.1101/090332 · Cited by 310 · see citing works
SPRING: a kinetic interface for visualizing high dimensional single-cell expression data
Weinreb, C., Wolock, S., & Klein, A. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx792 · Cited by 298 · see citing works
Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
Marco, E., Karp, R. L., Guo, G., Robson, P., Hart, A. H., Trippa, L., & Yuan, G.-C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408993111 · Cited by 268 · see citing works
CLF associates with CLC to form a functional heteromeric ligand for the CNTF receptor complex
Elson, G., Lelièvre, E., Guillet, C., Chevalier, S., Plun‐Favreau, H., Froger, J., Suard, I., de Coignac, A. B., Delneste, Y., Bonnefoy, J., et al. (2000) · Nature Neuroscience · doi:10.1038/78765 · Cited by 252 · see citing works
Scott, I. C., Anson‐Cartwright, L., Riley, P. R., Reda, D., & Cross, J. C. (2000) · Molecular and Cellular Biology · doi:10.1128/mcb.20.2.530-541.2000 · Cited by 229 · see citing works
Integrative Analyses of Human Reprogramming Reveal Dynamic Nature of Induced Pluripotency
Cacchiarelli, D., Trapnell, C., Ziller, M. J., Soumillon, M., Cesana, M., Karnik, R., Donaghey, J., Smith, Z. D., Ratanasirintrawoot, S., Zhang, X., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.016 · Cited by 228 · see citing works
Self-citationNfix Regulates Fetal-Specific Transcription in Developing Skeletal Muscle
Messina, G., Biressi, S., Monteverde, S., Magli, A., Cassano, M., Perani, L., Roncaglia, E., Tagliafico, E., Starnes, L., Campbell, C. E., et al. (2010) · Cell · doi:10.1016/j.cell.2010.01.027 · Cited by 210 · see citing works
A reprogrammable mouse strain from gene-targeted embryonic stem cells
Stadtfeld, M., Maherali, N., Borkent, M., & Hochedlinger, K. (2009) · Nature Methods · doi:10.1038/nmeth.1409 · Cited by 208 · see citing works
Self-citationWoo, S.-Y., Kim, D., Jun, C., Kim, Y. M., Haar, E. V., Lee, S.-I., Hegg, J. W., Bandhakavi, S., Griffin, T. J., & Kim, D. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m704343200 · Cited by 200 · see citing works
GATA-2 and GATA-3 regulate trophoblast-specific gene expression in vivo
Ma, G. T., Roth, M. E., Groskopf, J. C., Tsai, F., Orkin, S. H., Grosveld, F. G., Engel, J. D., & Linzer, D. I. H. (1997) · Development · doi:10.1242/dev.124.4.907 · Cited by 191 · see citing works
Regulation of the Pluripotency Marker Rex-1 by Nanog and Sox2
Shi, W., Wang, H., Pan, G., Geng, Y., Guo, Y., & Pei, D. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m601811200 · Cited by 189 · see citing works
Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M., & Nolan, G. P. (2015) · Cell stem cell · doi:10.1016/j.stem.2015.01.015 · Cited by 177 · see citing works
Sur la théorie relativiste de l'électron et l'interprétation de la mécanique quantique
Schrödinger, E. (1932) · French digital mathematics library (Numdam) · Cited by 175 · see citing works
Single-Cell Transcriptome Analysis Reveals Dynamic Changes in lncRNA Expression during Reprogramming
Kim, D. H., Marinov, G. K., Pepke, S., Singer, Z. S., He, P., Williams, B. A., Schroth, G. P., Elowitz, M. B., & Wold, B. J. (2015) · Cell stem cell · doi:10.1016/j.stem.2014.11.005 · Cited by 163 · see citing works
Scaling Algorithms for Unbalanced Transport ProblemsPreprint
Chizat, L., Peyré, G., Schmitzer, B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.05816 · Cited by 160 · see citing works
Single-Cell RNA-Seq Reveals Dynamic Early Embryonic-like Programs during Chemical Reprogramming
Zhao, T., Fu, Y., Zhu, J., Liu, Y., Zhang, Q., Yi, Z., Chen, S., Jiao, Z., Xu, X., Xu, J., et al. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.05.025 · Cited by 159 · see citing works
Wnt7b Regulates Placental Development in Mice
Parr, B. A., Cornish, V., Cybulsky, M. I., & McMahon, A. P. (2001) · Developmental Biology · doi:10.1006/dbio.2001.0373 · Cited by 151 · see citing works
High-resolution analysis with novel cell-surface markers identifies routes to iPS cells
O’Malley, A. J., Skylaki, S., Iwabuchi, K. A., Chantzoura, E., Ruetz, T. J., Johnsson, A., Tomlinson, S. R., Linnarsson, S., & Kaji, K. (2013) · Nature · doi:10.1038/nature12243 · Cited by 150 · see citing works
The oncogene c-Jun impedes somatic cell reprogramming
Liu, J., Han, Q., Peng, T., Peng, M., Wei, B., Li, D., Wang, X., Yu, S., Yang, J., Cao, S., et al. (2015) · Nature Cell Biology · doi:10.1038/ncb3193 · Cited by 146 · see citing works
Self-citationZic3 Is Required for Maintenance of Pluripotency in Embryonic Stem Cells
Lim, L. S., Loh, Y., Zhang, W., Li, Y., Chen, X., Wang, Y., Bakre, M. M., Ng, H., & Stanton, L. W. (2007) · Molecular Biology of the Cell · doi:10.1091/mbc.e06-07-0624 · Cited by 141 · see citing works
Kidder, B. L., & Palmer, S. S. (2010) · Genome Research · doi:10.1101/gr.101469.109 · Cited by 126 · see citing works
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation DistancesPreprint
Cuturi, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.0895 · Cited by 121 · see citing works
From the stem of the placental tree: trophoblast stem cells and their progeny
Latos, P. A., & Hemberger, M. (2016) · Development · doi:10.1242/dev.133462 · Cited by 120 · see citing works
Loss of Cited2 affects trophoblast formation and vascularization of the mouse placenta
Withington, S. L., Scott, A., Saunders, D. N., Floro, K. L., Preis, J. I., Michalicek, J., Maclean, K., Sparrow, D. B., Martı́nez-Barberá, J. P., & Dunwoodie, S. L. (2006) · Developmental Biology · doi:10.1016/j.ydbio.2006.02.025 · Cited by 112 · see citing works
X Chromosome Reactivation Dynamics Reveal Stages of Reprogramming to Pluripotency
Pasque, V., Tchieu, J. H., Karnik, R., Uyeda, M. J., Dimashkie, A., Case, D., Papp, B., Bonora, G., Patel, S. G., Ho, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.040 · Cited by 108 · see citing works
Wu, X., Oatley, J. M., Oatley, M. J., et al. (2010) · Biology of Reproduction · doi:10.1095/biolreprod.109.083097 · Cited by 106 · see citing works
Klauzińska, M., de Castro, N. P., Rangel, M. C., Spike, B. T., Gray, P. C., Bertolette, D. C., Cuttitta, F. F., & Salomon, D. S. (2014) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2014.08.003 · Cited by 104 · see citing works
A SURVEY OF THE SCHRODINGER PROBLEM AND SOME OF ITS CONNECTIONS WITH OPTIMAL TRANSPORTPreprint
(2016) · Cited by 103 · see citing works
TGF-beta in neural stem cells and in tumors of the central nervous system
Aigner, L., & Bogdahn, U. (2007) · Cell and Tissue Research · doi:10.1007/s00441-007-0466-7 · Cited by 100 · see citing works
PPARγ Regulates Trophoblast Proliferation and Promotes Labyrinthine Trilineage Differentiation
Parast, M. M., Yu, H. J., Ciric, A., Salata, M. W., DAVIS, V. M., & Milstone, D. S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008055 · Cited by 98 · see citing works
A comparison of single-cell trajectory inference methods: towards more accurate and robust toolsPreprint
Saelens, W., Cannoodt, R., Todorov, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276907 · Cited by 98 · see citing works
Tunster, S. J., Creeth, H., & John, R. M. (2015) · Developmental Biology · doi:10.1016/j.ydbio.2015.10.015 · Cited by 97 · see citing works
Histone chaperone ASF1A is required for maintenance of pluripotency and cellular reprogramming
González-Muñoz, E., Arboleda-Estudillo, Y., Otu, H. H., & Cibelli, J. B. (2014) · Science · doi:10.1126/science.1254745 · Cited by 88 · see citing works
Obox, a Family of Homeobox Genes Preferentially Expressed in Germ Cells
Rajkovic, A., Yan, C., Wei, Y., et al. (2002) · Genomics · doi:10.1006/geno.2002.6759 · Cited by 74 · see citing works
Pei, J., & Grishin, N. V. (2011) · Cellular Signalling · doi:10.1016/j.cellsig.2011.11.011 · Cited by 68 · see citing works
Selective Amplification of the Genome Surrounding Key Placental Genes in Trophoblast Giant Cells
Hannibal, R. L., & Baker, J. C. (2016) · Current Biology · doi:10.1016/j.cub.2015.11.060 · Cited by 62 · see citing works
OSKM Induce Extraembryonic Endoderm Stem Cells in Parallel to Induced Pluripotent Stem Cells
Parenti, A., Halbisen, M. A., Wang, K., Latham, K. E., & Ralston, A. (2016) · Stem Cell Reports · doi:10.1016/j.stemcr.2016.02.003 · Cited by 57 · see citing works
Wolf, F. A., Hamey, F., Plass, M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/208819 · Cited by 48 · see citing works
Wu, D., Hong, H., Huang, X., Huang, L., He, Z., Fang, Q., & Luo, Y. (2016) · Placenta · doi:10.1016/j.placenta.2016.04.016 · Cited by 42 · see citing works
Waddington, C. H. (2015) · Routledge eBooks · doi:10.4324/9781315665566 · Cited by 32 · see citing works
TASIC: determining branching models from time series single cell data
Rashid, S., Kotton, D. N., & Bar‐Joseph, Z. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx173 · Cited by 31 · see citing works
MSX2 Induces Trophoblast Invasion in Human Placenta
Liang, H., Zhang, Q., Lu, J., Yang, G., Tian, N., Wang, X., Tan, Y., & Tan, D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153656 · Cited by 28 · see citing works
An Integrative Framework Reveals Signaling-to-Transcription Events in Toll-like Receptor Signaling
Mertins, P., Przybylski, D., Yosef, N., Qiao, J., Clauser, K. R., Raychowdhury, R., Eisenhaure, T., Maritzen, T., Haucke, V., Satoh, T., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.016 · Cited by 28 · see citing works
Self-citationGegenschatz‐Schmid, K., Verkauskas, G., Demougin, P., et al. (2017) · Genes · doi:10.3390/genes8100267 · Cited by 23 · see citing works
Park, M., Lee, Y., Jang, H. S., et al. (2016) · Scientific Reports · doi:10.1038/srep20980 · Cited by 19 · see citing works
Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells
Liu, L. L., Brumbaugh, J., Bar‐Nur, O., Smith, Z. D., Stadtfeld, M., Meissner, A., Hochedlinger, K., & Franziska Michor, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.11.080 · Cited by 16 · see citing works
Self-citationMesenchyme associated transcription factor PRRX1: A key regulator of IPF fibroblast
Froidure, A., Marchal‐Duval, E., Ghanem, M., et al. (2016) · doi:10.1183/13993003.congress-2016.oa506 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Jordan R, Kinderlehrer D, and Otto F (1998). The variational formulation of the fokker. SIAM J. Math. Anal, 29(1): 1– 17.
Cited by
Sorted by citations
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Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267 · see citing works
Lineage tracing on transcriptional landscapes links state to fate during differentiation
Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D., & Klein, A. M. (2020) · Science · doi:10.1126/science.aaw3381 · Cited by 730 · see citing works
“We tested three recent approaches, Population Balance Analysis (PBA) 4 , WaddingtonOT (WOT) 5 , and FateID 7 for their ability to predict the fate of a cell choosing between neutrophil and monocyte fates.”
Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury
Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C., & Humphreys, B. D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005477117 · Cited by 650 · see citing works
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works
Chemical reprogramming of human somatic cells to pluripotent stem cells.
Guan, J., Wang, G., Wang, J., Zhang, Z., Fu, Y., Cheng, L., Meng, G., Lyu, Y., Zhu, J., Li, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-04593-5 · Cited by 388 · see citing works
A cellular and spatial map of the choroid plexus across brain ventricles and ages
Dani, N., Herbst, R. H., McCabe, C., Green, G. S., Kaiser, K., Head, J. P., Cui, J., Shipley, F. B., Jang, A., Dionne, D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.003 · Cited by 388 · see citing works
Self-citationThe triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321 · see citing works
Alignment and integration of spatial transcriptomics data
Zeira, R., Land, M., Strzalkowski, A., & Raphael, B. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01459-6 · Cited by 298 · see citing works
RNA velocity—current challenges and future perspectives
Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110282 · Cited by 271 · see citing works
Single cell transcriptomics of human epidermis identifies basal stem cell transition states
Wang, S., Drummond, M. L., Guerrero-Juarez, C. F., Tarapore, E., MacLean, A. L., Stabell, A. R., Wu, S. C., Gutierrez, G., That, B. T., Benavente, C. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18075-7 · Cited by 218 · see citing works
A single-embryo, single-cell time-resolved model for mouse gastrulation
Mittnenzweig, M., Mayshar, Y., Cheng, S., Ben-Yair, R., Hadas, R., Rais, Y., Chomsky, E., Reines, N., Uzonyi, A., Lumerman, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.004 · Cited by 214 · see citing works
Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
Kimmel, J. C., Penland, L., Rubinstein, N. D., Hendrickson, D. G., Kelley, D. R., & Rosenthal, A. Z. (2019) · Genome Research · doi:10.1101/gr.253880.119 · Cited by 184 · see citing works
“Optimal transport is a technique for measuring distances between equally sized samples that has been applied to a wide variety of data analysis tasks, including scRNA-seq (Schiebinger et al. 2019).”
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
Highly efficient and rapid generation of human pluripotent stem cells by chemical reprogramming.
Liuyang, S., Wang, G., Wang, Y., He, H., Lyu, Y., Cheng, L., Yang, Z., Guan, J., Fu, Y., Zhu, J., et al. (2023) · Cell Stem Cell · doi:10.1016/j.stem.2023.02.008 · Cited by 136 · see citing works
Li, C., Virgilio, M. C., Collins, K. L., & Welch, J. D. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01476-y · Cited by 130 · see citing works
Yeo, G. H. T., Saksena, S. D., & Gifford, D. K. (2021) · Nature Communications · doi:10.1038/s41467-021-23518-w · Cited by 103 · see citing works
Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA
Kobayashi-Kirschvink, K. J., Comiter, C. S., Gaddam, S., Joren, T., Grody, E. I., Ounadjela, J. R., Zhang, K., Ge, B., Kang, J. W., Xavier, R. J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02082-2 · Cited by 93 · see citing works
Self-citationMorphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Song, A. H., Chen, R. J., Ding, T., Williamson, D. F. K., Jaume, G., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01099 · Cited by 79 · see citing works
How do cells adapt? stories told in landscapes
Agozzino, L., Balázsi, G., Wang, J., & Dill, K. A. (2020) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-011720-103410 · Cited by 37 · see citing works
CMOT: cross-modality optimal transport for multimodal inference
Alatkar, S. A., & Wang, D. (2023) · Genome biology · doi:10.1186/s13059-023-02989-8 · Cited by 24 · see citing works
PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations
Yu, H., & Welch, J. D. (2022) · doi:10.1101/2022.07.20.500854 · Cited by 57 · see citing works
“To quantify the difference between the cell state distributions, we use Wasserstein distance, which has been widely used to quantify cell populations’ distance [29, 30, 31].”
Kriukov, D., Khrameeva, E. E., Gladyshev, V. N., Dmitriev, S. E., & Tyshkovskiy, A. (2022) · doi:10.1101/2022.12.12.520058 · Cited by 13 · see citing works
- Authors
- Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., Lee, L., Chen, J., Brumbaugh, J., Rigollet, P., Hochedlinger, K., Jaenisch, R., Regev, A., & Lander, E. S.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Howard Hughes Medical Institute (US)
- Whitehead Institute for Biomedical Research (US)
- Corresponding author
- Shu, Jian
- Regev, Aviv
- Lander, Eric S.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 176
- Issue
- 4
- Pages
- 928-943.e22
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6402800
- OpenAlex
- W2911279170
- Cited by
- 829
- References
- 81
- Citations by year
- 2026: 145 · 2025: 138 · 2024: 117 · 2023: 117 · 2022: 93 · 2021: 93 · 2020: 86 · 2019: 38 · 2018: 1
- FWCI
- 25.97
- Type
- journal article
- Language
- en
- Update notices
- erratum (2019-03-07)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- Broad Institute (CEGS)
- NIH (HD045022, R01 MH104610-15, R01NS088538, R01HD058013, K99HD096049, CASI)
- Helen Hay Whitney Foundation
- Klarman Cell Observatory
- Burroughs Wellcome Fund
- NSF (DMS-1712596, TRIPODS-1740751, IIS-1838071)
- ONR (N00014-17-1-2147)
- CZI (DAF 2018-182642)
- MIT Skoltech Seed Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- developmental trajectories, cellular reprogramming, optimal transport, single-cell gene expression, mesenchymal-epithelial transition, pluripotency, neural cells, transcription factors, GDF9, paracrine signaling, fate prediction
- MeSH terms
- Animals, Cell Differentiation, Cells, Cultured, Fibroblasts, Transcription Factors, Gene Expression, Sequence Analysis, RNA, Gene Expression Regulation, Developmental, Gene Expression Profiling, Mice, Embryonic Stem Cells, Induced Pluripotent Stem Cells, Single-Cell Analysis, Cellular Reprogramming
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